pacman::p_load(readxl, gifski, gapminder,
plotly, gganimate, tidyverse)Hands-on Exercise 3 - Programming Animated Statistical Graphics with R
Learning Objectives:
- Create animated data visualisation by using gganimate and plotly r packages
- Reshape data by using tidyr package
- Process, wrangle and transform data by using dplyr package
Animation Basic Concepts
When creating animations, many individual plots are built and then stitched together as movie frames like a flip book. Each frame is a different plot built with a relevant subset of the aggregate data. The subset drives the flow of the animation when stitched together.
Terminology
Frame: In an animated line graph, each frame represents a different point in time or a different category. When the frame changes, the data points on the graph are updated to reflect the new data.
Animation Attributes: The settings that control how the animation behaves. For example:
Duration of each frame
Easing function used between frame transitions
Start the animation from the current frame or from the beginning
Getting Started
Installing and loading the required libraries
The following R packages will be used:
plotly, R library for plotting interactive statistical graphs.
gganimate, an ggplot extension for creating animated statistical graphs.
gifski converts video frames to GIF animations using pngquant’s fancy features for efficient cross-frame palettes and temporal dithering. It produces animated GIFs that use thousands of colors per frame.
gapminder: An excerpt of the data available at Gapminder.org. We just want to use its country_colors scheme.
tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.
Importing the Data
The code chunk below imports GlobalPopulation.xlsx into R environment by using read_xls() function of readr package.
readr is a pacakge within tidyverse.
col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
sheet="Data") %>%
mutate_at(col, as.factor) %>%
mutate(Year = as.integer(Year))
Tip
Instead of using mutate_at(), across() can be used to derive the same output
Animated Data Visualisation: gganimate methods
gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. A range of new grammar classes that can be added to the plot object for customisation:
transition_*()defines how the data should be spread out and how it relates to itself across time.view_*()defines how the positional scales should change along the animation.shadow_*()defines how data from other points in time should be presented in the given point in time.enter_*()/exit_*()defines how new data should appear and how old data should disappear during the course of the animation.ease_aes()defines how different aesthetics should be eased during transitions.
Building a static population bubble plot
In the code chunk below, the basic ggplot2 functions are used to create a static bubble plot.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') Building the animated bubble plot
In the code chunk below,
transition_time()of gganimate is used to create transition through distinct states in time (i.e. Year).ease_aes()is used to control easing of aesthetics.The default is
linear.Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') +
transition_time(Year) +
ease_aes('linear') Animated Data Visualisation: plotly
In Plotly R package, both ggplotly() and plot_ly() support key frame animations through the frame argument/aesthetic. They also support an ids argument/aesthetic to ensure smooth transitions between objects with the same id (which helps facilitate object constancy).
Building an animated bubble plot: ggplotly() method
Create an animated bubble plot by using ggplotly() method.
Appropriate ggplot2 functions are used to create a static bubble plot. The output is then saved as an R object called gg.
ggplotly()is then used to convert the R graphic object into an animated svg object.
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young') +
theme(legend.position='none')
ggplotly(gg)Although show.legend = FALSE argument was used, the legend still appears on the plot. To overcome this problem, theme(legend.position='none') should be used
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young') +
theme(legend.position='none')
ggplotly(gg)Building an animated bubble plot: plot_ly() method
Create an animated bubble plot by using plot_ly() method.
bp <- globalPop %>%
plot_ly(x = ~Old,
y = ~Young,
size = ~Population,
color = ~Continent,
sizes = c(2, 100),
frame = ~Year,
text = ~Country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(showlegend = FALSE)
bp